Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-05-23 12:32:11 +02:00
parent e67cee4a0c
commit 3006c33c29
1 changed files with 7 additions and 8 deletions
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15
train.py
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@ -167,6 +167,10 @@ def train(
from apex import amp from apex import amp
model, optimizer = amp.initialize(model, optimizer, opt_level='O1') model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
# Remove old results
for f in glob.glob('train_batch*.jpg') + glob.glob('test_batch*.jpg') + 'results.txt':
os.remove(f)
# Start training # Start training
model.hyp = hyp # attach hyperparameters to model model.hyp = hyp # attach hyperparameters to model
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
@ -175,8 +179,6 @@ def train(
maps = np.zeros(nc) # mAP per class maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0) # P, R, mAP, F1, test_loss results = (0, 0, 0, 0, 0) # P, R, mAP, F1, test_loss
n_burnin = min(round(nb / 5 + 1), 1000) # burn-in batches n_burnin = min(round(nb / 5 + 1), 1000) # burn-in batches
for f in glob.glob('train_batch*.jpg') + glob.glob('test_batch*.jpg'):
os.remove(f)
t, t0 = time.time(), time.time() t, t0 = time.time(), time.time()
for epoch in range(start_epoch, epochs): for epoch in range(start_epoch, epochs):
model.train() model.train()
@ -185,7 +187,7 @@ def train(
# Update scheduler # Update scheduler
scheduler.step() scheduler.step()
# Freeze backbone at epoch 0, unfreeze at epoch 1 # Freeze backbone at epoch 0, unfreeze at epoch 1 (optional)
if freeze_backbone and epoch < 2: if freeze_backbone and epoch < 2:
for name, p in model.named_parameters(): for name, p in model.named_parameters():
if int(name.split('.')[1]) < cutoff: # if layer < 75 if int(name.split('.')[1]) < cutoff: # if layer < 75
@ -200,7 +202,6 @@ def train(
for i, (imgs, targets, _, _) in enumerate(dataloader): for i, (imgs, targets, _, _) in enumerate(dataloader):
imgs = imgs.to(device) imgs = imgs.to(device)
targets = targets.to(device) targets = targets.to(device)
nt = len(targets)
# Plot images with bounding boxes # Plot images with bounding boxes
if epoch == 0 and i == 0: if epoch == 0 and i == 0:
@ -233,13 +234,11 @@ def train(
optimizer.step() optimizer.step()
optimizer.zero_grad() optimizer.zero_grad()
# Update running mean of tracked metrics
mloss = (mloss * i + loss_items) / (i + 1)
# Print batch results # Print batch results
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
s = ('%8s%12s' + '%10.3g' * 7) % ( s = ('%8s%12s' + '%10.3g' * 7) % (
'%g/%g' % (epoch, epochs - 1), '%g/%g' % (epoch, epochs - 1),
'%g/%g' % (i, nb - 1), *mloss, nt, time.time() - t) '%g/%g' % (i, nb - 1), *mloss, len(targets), time.time() - t)
t = time.time() t = time.time()
print(s) print(s)